Application of Deep Neural Networks to Predict Dynamic Stability of Power System - Phan Viết Thịnh; Nguyễn Ngọc Âu (Journal of Technical Education Science, 2024)
Electricity demand is increasing, transmission line development can not keep up with it. This puts the power system in a full load state which puts the power system operating near the boundary of stability. During operation, large disturbances cause power imbalance and voltage drops which cause instability. It is vital to detect the power system dynamic instability quickly. It prevents the disintegration of the power grid leading to widespread power outages which results in great economic losses. Traditional analysis methods are slow in making control decisions. Artificial neural networks overcome this drawback because they calculate quickly and accurately. This paper applies deep neural networks to predict power system dynamic stability. Evaluated on the IEEE 39bus power system data set, the deep neural networks have a validation accuracy as high as 96.99%. Compared with perceptron neural networks, deep neural networks have 1.5% higher validation accuracy.